Olivia
Online
tricky_fast
@tricky_fast
Twitter'a katıldığı tarih December 2021
104 Takip Edilen
86 Takipçi
413 Gönderi
Dr. LeCun's heavily promoted Joint Embedding Predictive Architecture (JEPA, 2022) [5] is the heart of his new company. However, the core ideas are not original to LeCun. Instead, JEPA is essentially identical to our 1992 Predictability Maximization system (PMAX) [1][14].
Details in reference [19] which contains many additional references.
Motivation of PMAX [1][14]. Since details of inputs are often unpredictable from related inputs, two non-generative artificial neural networks interact as follows: one net tries to create a non-trivial, informative, latent representation of its own input that is predictable from the latent representation of the other net’s input.
PMAX [1][14] is actually a whole family of methods. Consider the simplest instance in Sec. 2.2 of [1]: an auto encoder net sees an input and represents it in its hidden units (its latent space). The other net sees a different but related input and learns to predict (from its own latent space) the auto encoder's latent representation, which in turn tries to become more predictable, without giving up too much information about its own input, to prevent what's now called “collapse." See illustration 5.2 in Sec. 5.5 of [14] on the "extraction of predictable concepts."
The 1992 PMAX paper [1] discusses not only auto encoders but also other techniques for encoding data. The experiments were conducted by my student Daniel Prelinger. The non-generative PMAX outperformed the generative IMAX [2] on a stereo vision task.
The 2020 BYOL [10] is also closely related to PMAX. In 2026, @misovalko, leader of the BYOL team, praised PMAX, and listed numerous similarities to much later work [19].
Note that the self-created “predictable classifications” in the title of [1] (and the so-called “outputs” of the entire system [1]) are typically INTERNAL "distributed representations” (like in the title of Sec. 4.2 of [1]).
The 1992 PMAX paper [1] considers both symmetric and asymmetric nets. In the symmetric case, both nets are constrained to emit "equal (and therefore mutually predictable)" representations [1]. Sec. 4.2 on “finding predictable distributed representations” has an experiment with 2 weight-sharing auto encoders which learn to represent in their latent space what their inputs have in common (see the cover image of this post).
Of course, back then compute was was a million times more expensive, but the fundamental insights of "JEPA" were present, and LeCun has simply repackaged old ideas without citing them [5,6,19].
This is hardly the first time LeCun (or others writing about him) have exaggerated LeCun's own significance by downplaying earlier work. He did NOT "co-invent deep learning" (as some know-nothing "AI influencers" have claimed) [11,13], and he did NOT invent convolutional neural nets (CNNs) [12,6,13], NOR was he even the first to combine CNNs with backpropagation [12,13]. While he got awards for the inventions of other researchers whom he did not cite [6], he did not invent ANY of the key algorithms that underpin modern AI [5,6,19].
LeCun's recent pitch: 1. LLMs such as ChatGPT are insufficient for AGI (which has been obvious to experts in AI & decision making, and is something he once derided @GaryMarcus for pointing out [17]). 2. Neural AIs need what I baptized a neural "world model" in 1990 [8][15] (earlier, less general neural nets of this kind, such as those by Paul Werbos (1987) and others [8], weren't called "world models," although the basic concept itself is ancient [8]). 3. The world model should learn to predict (in non-generative "JEPA" fashion [5]) higher-level predictable abstractions instead of raw pixels: that's the essence of our 1992 PMAX [1][14].
Astonishingly, PMAX or "JEPA" seems to be the unique selling proposition of LeCun's 2026 company on world model-based AI in the physical world, which is apparently based on what we published over 3 decades ago [1,5,6,7,8,13,14], and modeled after our 2014 company on world model-based AGI in the physical world [8].
In short, little if anything in JEPA is new [19]. But then the fact that LeCun would repackage old ideas and present them as his own clearly isn't new either [5,6,18,19].
FOOTNOTES
1. Note that PMAX is NOT the 1991 adversarial Predictability MINimization (PMIN) [3,4]. However, PMAX may use PMIN as a submodule to create informative latent representations [1](Sec. 2.4), and to prevent what's now called “collapse." See the illustration on page 9 of [1].
2. Note that the 1991 PMIN [3] also predicts parts of latent space from other parts. However, PMIN's goal is to REMOVE mutual predictability, to obtain maximally disentangled latent representations called factorial codes. PMIN by itself may use the auto encoder principle in addition to its latent space predictor [3].
3. Neither PMAX nor PMIN was my first non-generative method for predicting latent space, which was published in 1991 in the context of neural net distillation [9]. See also [5-8].
4. While the cognoscenti agree that LLMs are insufficient for AGI, JEPA is so, too. We should know: we have had it for over 3 decades under the name PMAX! Additional techniques are required to achieve AGI, e.g., meta learning, artificial curiosity and creativity, efficient planning with world models, and others [16].
REFERENCES (easy to find on the web):
[1] J. Schmidhuber (JS) & D. Prelinger (1993). Discovering predictable classifications. Neural Computation, 5(4):625-635. Based on TR CU-CS-626-92 (1992): https://t.co/wJFbdPhwdi
[2] S. Becker, G. E. Hinton (1989). Spatial coherence as an internal teacher for a neural network. TR CRG-TR-89-7, Dept. of CS, U. Toronto.
[3] JS (1992). Learning factorial codes by predictability minimization. Neural Computation, 4(6):863-879. Based on TR CU-CS-565-91, 1991.
[4] JS, M. Eldracher, B. Foltin (1996). Semilinear predictability minimization produces well-known feature detectors. Neural Computation, 8(4):773-786.
[5] JS (2022-23). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015.
[6] JS (2023-25). How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23.
[7] JS (2026). Simple but powerful ways of using world models and their latent space. Opening keynote for the World Modeling Workshop, 4-6 Feb, 2026, Mila - Quebec AI Institute.
[8] JS (2026). The Neural World Model Boom. Technical Note IDSIA-2-26.
[9] JS (1991). Neural sequence chunkers. TR FKI-148-91, TUM, April 1991. (See also Technical Note IDSIA-12-25: who invented knowledge distillation with artificial neural networks?)
[10] J. Grill et al (2020). Bootstrap your own latent: A "new" approach to self-supervised Learning. arXiv:2006.07733
[11] JS (2025). Who invented deep learning? Technical Note IDSIA-16-25.
[12] JS (2025). Who invented convolutional neural networks? Technical Note IDSIA-17-25.
[13] JS (2022-25). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA-22-22, arXiv:2212.11279
[14] JS (1993). Network architectures, objective functions, and chain rule. Habilitation Thesis, TUM. See Sec. 5.5 on "Vorhersagbarkeitsmaximierung" (Predictability Maximization).
[15] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM.
[16] JS (1990-2026). AI Blog.
[17] @GaryMarcus. Open letter responding to @ylecun. A memo for future intellectual historians. Substack, June 2024.
[18] G. Marcus. The False Glorification of @ylecun. Don’t believe everything you read. Substack, Nov 2025.
[19] J. Schmidhuber. Who invented JEPA? Technical Note IDSIA-3-22, IDSIA, Switzerland, March 2026. https://t.co/fDauPE6T2N
![SchmidhuberAI's tweet photo. Dr. LeCun's heavily promoted Joint Embedding Predictive Architecture (JEPA, 2022) [5] is the heart of his new company. However, the core ideas are not original to LeCun. Instead, JEPA is essentially identical to our 1992 Predictability Maximization system (PMAX) [1][14].
Details in reference [19] which contains many additional references.
Motivation of PMAX [1][14]. Since details of inputs are often unpredictable from related inputs, two non-generative artificial neural networks interact as follows: one net tries to create a non-trivial, informative, latent representation of its own input that is predictable from the latent representation of the other net’s input.
PMAX [1][14] is actually a whole family of methods. Consider the simplest instance in Sec. 2.2 of [1]: an auto encoder net sees an input and represents it in its hidden units (its latent space). The other net sees a different but related input and learns to predict (from its own latent space) the auto encoder's latent representation, which in turn tries to become more predictable, without giving up too much information about its own input, to prevent what's now called “collapse." See illustration 5.2 in Sec. 5.5 of [14] on the "extraction of predictable concepts."
The 1992 PMAX paper [1] discusses not only auto encoders but also other techniques for encoding data. The experiments were conducted by my student Daniel Prelinger. The non-generative PMAX outperformed the generative IMAX [2] on a stereo vision task.
The 2020 BYOL [10] is also closely related to PMAX. In 2026, @misovalko, leader of the BYOL team, praised PMAX, and listed numerous similarities to much later work [19].
Note that the self-created “predictable classifications” in the title of [1] (and the so-called “outputs” of the entire system [1]) are typically INTERNAL "distributed representations” (like in the title of Sec. 4.2 of [1]).
The 1992 PMAX paper [1] considers both symmetric and asymmetric nets. In the symmetric case, both nets are constrained to emit "equal (and therefore mutually predictable)" representations [1]. Sec. 4.2 on “finding predictable distributed representations” has an experiment with 2 weight-sharing auto encoders which learn to represent in their latent space what their inputs have in common (see the cover image of this post).
Of course, back then compute was was a million times more expensive, but the fundamental insights of "JEPA" were present, and LeCun has simply repackaged old ideas without citing them [5,6,19].
This is hardly the first time LeCun (or others writing about him) have exaggerated LeCun's own significance by downplaying earlier work. He did NOT "co-invent deep learning" (as some know-nothing "AI influencers" have claimed) [11,13], and he did NOT invent convolutional neural nets (CNNs) [12,6,13], NOR was he even the first to combine CNNs with backpropagation [12,13]. While he got awards for the inventions of other researchers whom he did not cite [6], he did not invent ANY of the key algorithms that underpin modern AI [5,6,19].
LeCun's recent pitch: 1. LLMs such as ChatGPT are insufficient for AGI (which has been obvious to experts in AI & decision making, and is something he once derided @GaryMarcus for pointing out [17]). 2. Neural AIs need what I baptized a neural "world model" in 1990 [8][15] (earlier, less general neural nets of this kind, such as those by Paul Werbos (1987) and others [8], weren't called "world models," although the basic concept itself is ancient [8]). 3. The world model should learn to predict (in non-generative "JEPA" fashion [5]) higher-level predictable abstractions instead of raw pixels: that's the essence of our 1992 PMAX [1][14].
Astonishingly, PMAX or "JEPA" seems to be the unique selling proposition of LeCun's 2026 company on world model-based AI in the physical world, which is apparently based on what we published over 3 decades ago [1,5,6,7,8,13,14], and modeled after our 2014 company on world model-based AGI in the physical world [8].
In short, little if anything in JEPA is new [19]. But then the fact that LeCun would repackage old ideas and present them as his own clearly isn't new either [5,6,18,19].
FOOTNOTES
1. Note that PMAX is NOT the 1991 adversarial Predictability MINimization (PMIN) [3,4]. However, PMAX may use PMIN as a submodule to create informative latent representations [1](Sec. 2.4), and to prevent what's now called “collapse." See the illustration on page 9 of [1].
2. Note that the 1991 PMIN [3] also predicts parts of latent space from other parts. However, PMIN's goal is to REMOVE mutual predictability, to obtain maximally disentangled latent representations called factorial codes. PMIN by itself may use the auto encoder principle in addition to its latent space predictor [3].
3. Neither PMAX nor PMIN was my first non-generative method for predicting latent space, which was published in 1991 in the context of neural net distillation [9]. See also [5-8].
4. While the cognoscenti agree that LLMs are insufficient for AGI, JEPA is so, too. We should know: we have had it for over 3 decades under the name PMAX! Additional techniques are required to achieve AGI, e.g., meta learning, artificial curiosity and creativity, efficient planning with world models, and others [16].
REFERENCES (easy to find on the web):
[1] J. Schmidhuber (JS) & D. Prelinger (1993). Discovering predictable classifications. Neural Computation, 5(4):625-635. Based on TR CU-CS-626-92 (1992): https://t.co/wJFbdPhwdi
[2] S. Becker, G. E. Hinton (1989). Spatial coherence as an internal teacher for a neural network. TR CRG-TR-89-7, Dept. of CS, U. Toronto.
[3] JS (1992). Learning factorial codes by predictability minimization. Neural Computation, 4(6):863-879. Based on TR CU-CS-565-91, 1991.
[4] JS, M. Eldracher, B. Foltin (1996). Semilinear predictability minimization produces well-known feature detectors. Neural Computation, 8(4):773-786.
[5] JS (2022-23). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 1990-2015.
[6] JS (2023-25). How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23.
[7] JS (2026). Simple but powerful ways of using world models and their latent space. Opening keynote for the World Modeling Workshop, 4-6 Feb, 2026, Mila - Quebec AI Institute.
[8] JS (2026). The Neural World Model Boom. Technical Note IDSIA-2-26.
[9] JS (1991). Neural sequence chunkers. TR FKI-148-91, TUM, April 1991. (See also Technical Note IDSIA-12-25: who invented knowledge distillation with artificial neural networks?)
[10] J. Grill et al (2020). Bootstrap your own latent: A "new" approach to self-supervised Learning. arXiv:2006.07733
[11] JS (2025). Who invented deep learning? Technical Note IDSIA-16-25.
[12] JS (2025). Who invented convolutional neural networks? Technical Note IDSIA-17-25.
[13] JS (2022-25). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA-22-22, arXiv:2212.11279
[14] JS (1993). Network architectures, objective functions, and chain rule. Habilitation Thesis, TUM. See Sec. 5.5 on "Vorhersagbarkeitsmaximierung" (Predictability Maximization).
[15] JS (1990). Making the world differentiable: On using fully recurrent self-supervised neural networks for dynamic reinforcement learning and planning in non-stationary environments. Technical Report FKI-126-90, TUM.
[16] JS (1990-2026). AI Blog.
[17] @GaryMarcus. Open letter responding to @ylecun. A memo for future intellectual historians. Substack, June 2024.
[18] G. Marcus. The False Glorification of @ylecun. Don’t believe everything you read. Substack, Nov 2025.
[19] J. Schmidhuber. Who invented JEPA? Technical Note IDSIA-3-22, IDSIA, Switzerland, March 2026. https://t.co/fDauPE6T2N](https://pbs.twimg.com/media/HEvtVIRWAAEyz0C.jpg)
@Vrom14286662 wie kann man das stronghold export file importieren ? danke!
IOTA is back and better than ever!
@IOTA has been a cornerstone in the Web3 space for almost a decade, and now it's evolving into a next-gen network with 50K+ TPS, dual smart contract support (MoveVM & EVM), and decentralisation at its core.
What to expect?
- 50K+ TPS & 500ms confirmations
- Fair tokenomics – no VC allocations
- Up to 15% Staking APY
- Fee-burning mechanism 🔥
IOTA's real-world impact is huge, from supply chain to identity verification, and it’s now positioned for massive adoption in DeFi and beyond.
My $IOTA bag got hit like anything else overnight, yet I'm expecting their roadmap next months give solid upside
@iota 🤔 Universal Apps that natively extend to IOTA... We should talk!
$IOTA is looking very good.
On the high timeframe, it's holding macro support and now forming a deviation on the lower timeframe.
Any dip should be a buy on this.
Time to load some $IOTA here at the lower band of this ascending channel. 📈
Dips are a gift on this one, so I will not miss it! 🎁
Who would have thought I'd be tweeting about $HBAR and $IOTA in the year 2025...
$IOTA will go moon again from here 🚀✍️
Already i shared in my premium group ✅
Some recent big bags we’ve bought:
$ZENT - @zentry
$IOTA - @iota
$AVAX - @avax
$QANX - @QANplatform
$WMTX - @wmtoken
$CHEX - @ChintaiNetwork
$XBG - @XBorgHQ
$BEAM + 4 Nodes - @BeamFDN
$BIOHACK - @bio_hacker_dao
$BOTIFY - @BotifydotCloud
+ Tons of $XRP memecoins
You do what you want!
I’m just sharing what @megbzk and I are doing with our brand new bags.
$IOTA is really going to hit $1 this month, isn't it?
Just got the perfect BUY signal as well...
These #RWA tokens have the biggest potential this year👇:
$IOTA ~
@iota
$BKN ~
@Brickken
$UBXS ~
@Bixosinc
$SOIL ~
@soil_farm
$YBR ~
@YieldBricks
$WELF ~
@WelfFinance
$RIO ~
@realio_network
$PROPS ~
@PropbaseApp
$CREDI ~
@credefi_finance
$PROPC ~
@PropChainGlobal
$CARR ~
@carnomaly
Which of these do you have a bag in?💰
$IOTA looks promising, poised to break out from a second re-accumulation following the three-year-long accumulation below. 🚀
The upside potential is impressive once this massive consolidation phase ends. 💪
$IOTA is back in my portfolio
Why?
- the first blockchain to support MoveVM aside from $SUI & $APT
- the first to support both EVM & MoveVM
- # of users and dev activity growing
- IOTA rebased is super promising
- L1s are always a great narrative
- …
more reasons 👇
$IOTA / $USD - Update
Flip that level and we are good to go for the bulls
IOTA is #1 in my human heart
just sayin'
https://t.co/DKzN5VORP2
Rebased is the largest network upgrade for @iota, completely redefining it.
It marks a new era focused on RWA and tokenization.
My bullish thesis on $IOTA 🧵
#IOTA PRICE PREDICTION $10🔥| IOTA CRYPTO PREDICTION🔥| IOTA PREDICTION 🚀| IOTA
Sotwe'de En Son Ziyaret Edilenler
FratX
Son görüldüğü yer France
ASK PEŞİNDE KOŞAN
Son görüldüğü yer Turkey
𝐒𝐇𝐈𝐙𝐔𝐎 静男 🔻
Son görüldüğü yer Netherlands
Ligia Pessotto 
TheTruth
Son görüldüğü yer Japan
luna 🫶
Son görüldüğü yer United States
RENEE COX
Son görüldüğü yer United States
stwcrotcrot
Son görüldüğü yer Indonesia
duda
Son görüldüğü yer United States
พี่อชิ🎼🎸
Son görüldüğü yer United States
Senin İçin Trendler
En Popüler Kullanıcılar
Elon Musk
@elonmusk
241.5M takipçi
Barack Obama
@barackobama
118.8M takipçi
Cristiano Ronaldo
@cristiano
114.6M takipçi
Donald J. Trump
@realdonaldtrump
111.8M takipçi
Narendra Modi
@narendramodi
107.1M takipçi
Rihanna
@rihanna
98.7M takipçi
NASA
@nasa
92.3M takipçi
Justin Bieber
@justinbieber
91.8M takipçi
KATY PERRY
@katyperry
90.1M takipçi
Taylor Swift
@taylorswift13
84M takipçi
Lady Gaga
@ladygaga
75.5M takipçi
Virat Kohli
@imvkohli
73.5M takipçi
Kim Kardashian
@kimkardashian
70.9M takipçi
YouTube
@youtube
68.8M takipçi
Neymar Jr
@neymarjr
66.5M takipçi
Bill Gates
@billgates
65.3M takipçi
Selena Gomez
@selenagomez
63.1M takipçi
The Ellen Show
@theellenshow
62.2M takipçi
CNN
@cnn
61.7M takipçi
X
@x
60.7M takipçi